scholarly journals Determination of Optimal Number of Servers at Network Queuing Nodes to Reduce Waiting Time in a Tertiary Institution Clinic in Bida, Nigeria

2020 ◽  
Vol 24 (9) ◽  
pp. 1631-1639
Author(s):  
I. Muhammad ◽  
L. Adamu
Keyword(s):  
Waiting Time ◽  
Current System ◽  
Optimal Number ◽  
Queuing System ◽  
Tertiary Institution ◽  
Queuing Model ◽  
Direct Observations ◽  
Expected Waiting Time ◽  
System Nodes

In this paper, a network queuing model that determines optimal numbers of servers at the nodes of the school clinic network queuing system to  reduce waiting time of the patients has been presented. The relevant data was collected for a period four weeks, through direct observations and interviews. The number of arrivals and departures were also obtained. The total expected waiting time of the patient in the current system before modification was 50minutes with total number of 10 servers in all the nodes, while the total new expected waiting time of patient in the system after modification was reduced to 19 minutes with total number of 17 servers in all the nodes. The study has determined optimal number of servers at the nodes of the school clinic network system. Results from this study is an important information to the management of the school clinic for proper planning and better service delivery. Keywords: Network Queuing System, Nodes, Servers, School Clinic.

scholarly journals Applications of Queuing Theory in Quantitative Business Analysis

2020 ◽  
pp. 253-257
Keyword(s):  
Quantitative Analysis ◽  
Waiting Time ◽  
Queuing Theory ◽  
Single Channel ◽  
Analysis Approach ◽  
Single Server ◽  
Expected Number ◽  
Queuing Model ◽  
Infinite Population ◽  
Expected Waiting Time

Queuing Theory provides the system of applications in many sectors in life cycle. Queuing Structure and basic components determination is computed in queuing model simulation process. Distributions in Queuing Model can be extracted in quantitative analysis approach. Differences in Queuing Model Queue discipline, Single and Multiple service station with finite and infinite population is described in Quantitative analysis process. Basic expansions of probability density function, Expected waiting time in queue, Expected length of Queue, Expected size of system, probability of server being busy, and probability of system being empty conditions can be evaluated in this quantitative analysis approach. Probability of waiting ‘t’ minutes or more in queue and Expected number of customer served per busy period, Expected waiting time in System are also computed during the Analysis method. Single channel model with infinite population is used as most common case of queuing problems which involves the single channel or single server waiting line. Single Server model with finite population in test statistics provides the Relationships used in various applications like Expected time a customer spends in the system, Expected waiting time of a customer in the queue, Probability that there are n customers in the system objective case, Expected number of customers in the system

Development of a Heuristics Model for Simplifying a Multi-Channel Queuing System

2011 ◽  
Vol 367 ◽  
pp. 647-652
Author(s):  
B. Kareem ◽  
A. A. Aderoba
Keyword(s):  
Waiting Time ◽  
Channel Model ◽  
Single Channel ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Queuing Models ◽  
Significant Difference ◽  
Number Of Customers ◽  
Proportionality Principle

Queuing model has been discussed widely in literature. The structures of queuing systems are broadly divided into three namely; single, multi-channel, and mixed. Equations for solving these queuing problems vary in complexity. The most complex of them is the multi-channel queuing problem. A heuristically simplified equation based on relative comparison, using proportionality principle, of the measured effectiveness from the single and multi-channel models seems promising in solving this complex problem. In this study, six different queuing models were used from which five of them are single-channel systems while the balance is multi-channel. Equations for solving these models were identified based on their properties. Queuing models’ performance parameters were measured using relative proportionality principle from which complexity of multi-channel system was transformed to a simple linear relation of the form = . This showed that the performance obtained from single channel model has a linear relationship with corresponding to multi-channel, and is a factor which varies with the structure of queuing system. The model was tested with practical data collected on the arrival and departure of customers from a cocoa processing factory. The performances obtained based on average number of customers on line , average number of customers in the system , average waiting time in line and average waiting time in the system, under certain conditions showed no significant difference between using heuristics and analytical models.

scholarly journals Minimal cost service rate in priority queuing models for emergency cases in hospitals

2018 ◽  
Vol 6 (2) ◽  
pp. 50
Author(s):  
Nse S. Udoh ◽  
Idorenyin A. Etukudo
Keyword(s):  
Waiting Time ◽  
Service Rate ◽  
Time Cost ◽  
Service Cost ◽  
Queuing Model ◽  
Total Cost ◽  
Lower Priority ◽  
Queuing Models ◽  
Optimum Cost ◽  
Expected Waiting Time

Performance measures and waiting time cost for higher priority patients with severe cases over lower priority patients with stable cases using preemptive priority queuing model were obtained. Also, a total expected waiting time cost per unit time for service and the expected service cost per unit time for priority queuing models: M/M/2: ∞/NPP and M/M/2: ∞/PP were respectively formulated and optimized to obtain optimum cost service rate that minimizes the total cost. The results were applied to obtain optimum service rate that minimizes the total cost of providing and waiting for service at the emergency consulting unit of hospital.    

scholarly journals Deskripsi Sistem Antrian pada Klinik Dokter Spesialis Penyakit Dalam

2012 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Deiby T Salaki
Keyword(s):  
Direct Observation ◽  
Waiting Time ◽  
Arrival Rate ◽  
Queuing System ◽  
Queuing Model ◽  
Average Waiting Time ◽  
Number Of Patients

DESKRIPSI SISTEM ANTRIAN PADA KLINIK DOKTER SPESIALIS PENYAKIT DALAM ABSTRAK Penelitian ini dilakukan untuk mengetahui deskripsi sistem antrian pada klinik dokter internist. Pengumpulan data dilakukan secara langsung pada klinik dokter internist JHA selama 12 hari, selama 2 jam waktu pengamatan tiap harinya pada periode sibuk.. Model antrian yang digunakan adalah model (M/M/1) : (FIFO/~/~), tingkat kedatangan bersebaran poisson, waktu pelayanan bersebaran eksponensial, dengan jumlah pelayanan adalah seorang dokter, disiplin antrian yang digunakan adalah pasien yang pertama datang yang pertama dilayani, jumlah pelayanan dalam sistem dan ukuran populasi pada sumber masukan adalah tak berhingga.  Sistem antrian pada klinik ini memiliki kecepatan kedatangan pelayanan anamnesa rata-rata  menit 1 orang pasien datang, kecepatan kedatangan pelayanan pemeriksaan fisik rata-rata  menit 1 orang pasien datang, rata-rata waktu pelayanan anamnesa untuk  seorang pasien  menit, rata-rata waktu pelayanan pemeriksaan fisik untuk  seorang pasien  menit, peluang kesibukan  pelayanan anamnesa sebesar , peluang kesibukan  pelayanan pemeriksaan fisik sebesar , dan peluang pelayanan anamnesa menganggur sebesar , peluang pelayanan pemeriksaan fisik menganggur sebesar . Rata-rata banyaknya pengantri untuk anamnesa adalah  pasien sedangkan untuk pemeriksaan fisik  pasien, rata-rata banyaknya pengantri dalam sistem adalah  pasien, waktu rata-rata seorang pasien dalam klinik adalah  menit, waktu rata-rata seseorang pasien untuk antri adalah  menit. Kata kunci: Sistem Antrian, Klinik Penyakit Dalam  DESCRIPTION OF QUEUING SYSTEM AT THE INTERNIST CLINIC ABSTRACT This research determines the description of queuing system at the internist Clinic. Data collected by direct observation during 12 days and in 2 hours. Queuing model that used is model of (M/M/1): (FIFO /~/~). Based on the research, the clinic has 3.256 minutes per patient in average arrival rate for anamnesys, the average arrival rate for diaagnosys is 3.255 minutes per patient, average service speed for anamnesys is 2.675 minutes per patient, average service speed for diagnosys is 12.635 minutes, the probability of busy periods for anamnesys is 0.864, the probability of busy periods for diagnosys is 0.832 and probability of all free services or no patient in the anamnesys equal to 0.136, probability of all free services or no patient in the anamnesys equal to 0.168. The average number of patients in anamnesys queue is 5 patients, the average number of patients in diagnosys queue is 4 patients, the average number of patients in the system is 10 patients, the average waiting time in the system is 47.078 minutes and the average queuing time is 31.660 minutes. Keywords: Queuing system, internist clinic

scholarly journals Analysis of Landing Airplane Queue Systems at Juanda International Airport Surabaya

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 49-63
Author(s):  
Yuniar Farida ◽  
Fadilah Akbar ◽  
Moh. Hafiyusholeh ◽  
Moh. Hartono
Keyword(s):  
Waiting Time ◽  
Construction Project ◽  
Queuing System ◽  
Service Performance ◽  
Queuing Model ◽  
International Airport ◽  
Flight Frequency

Juanda International Airport is currently preparing to realize the construction of terminal 3. This construction project impression that Juanda Airport is experiencing an overload, including in the airplane queue. This study aims to analyze the current queuing system at the Juanda International Airport apron, whether effective, quite effective, or less effective in serving the number of existing flights with two terminals. An analysis of the queuing system was conducted in several scenarios. They are in normal/regular condition, a scenario if there is an increase in flight frequency, and a scenario if there is a reduction in aprons’ number because of certain exceptional situations. To analyze the airplane’s landing queue at Juanda airport apron, the queuing model (M/M/51) : (FCFS/∞/∞) is used. From this model, the results show that in normal conditions, the estimated waiting time for each airplane in the system is 0.18 hours with a queue of 2 up to 3 planes/hour, categorized as effective. In one apron reduction scenario, each airplane’s estimated waiting time in the system is 0.7 hours, with a queue of 6 up to 7 planes categorized as less effective. In the scenario of additional flights, only 9 other flights are allowed every day to keep the service performance still quite effective. By obtaining this results analysis, the decision of PT. Angkasa Pura 1 (Persero) to build terminal 3 is suitable to reduce queuing time and improve Juanda International Airport services to be more effective.

scholarly journals ANALISIS SISTEM ANTRIAN DAN OPTIMALISASI LAYANAN PUBLIK DI KANTOR PELAYANAN PAJAK PRATAMA PURWOKERTO

Performance ◽  
2017 ◽  
Vol 23 (2) ◽  
pp. 16
Author(s):  
Yusyida Munsa Idah ◽  
Didit Suhartono
Keyword(s):  
Data Collection ◽  
Waiting Time ◽  
Arrival Rate ◽  
Service Level ◽  
Queuing System ◽  
Second Step ◽  
Optimal Amount ◽  
The Third ◽  
Direct Observations ◽  
Independent Variable

The purpose of this study was to analyze the queuing system to provide optimum service to the community in Kantor Pelayananan Pajak Pratama Purwokerto. Data collection techniques to collect raw data obtained from direct observations of variables queuing system on the service tax at Kantor Pelayanan Pajak Pratama Purwokerto by measuring the speed of arrival of the taxpayer as well as the long service he received at each service. The variables used in this study is the dependent variable (dependent variable) is the queue, and the independent variable (independent variable) is the arrival rate taxpayer (λ), service level (μ), the optimal amount of services.The first step is to book study method is to collect, observe, analyze relevant theories. The second step is making direct observation by measuring the speed of arrival and departure of the taxpayer as well as the long pelayananan received at each service . The third step analyzing the arrival of the taxpayer in each service processed with frequency intervals of 10 minutes to find the number of arrivals and keberangktan the unity of time ( λ ) After doing the research results that the average waiting time taxpayer in the system and in the queue already meet specified target is 5 to 10 minutes .

Studying the appointment scheduling window considering patient no-show behavior in one public and one private outpatient clinics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohsen Abdoli ◽  
Mostafa Zandieh ◽  
Sajjad Shokouhyar
Keyword(s):  
Waiting Time ◽  
Queuing System ◽  
System Capacity ◽  
Outpatient Clinics ◽  
Effective Parameters ◽  
Queuing Model ◽  
Public And Private ◽  
Content Type ◽  
Total Cost ◽  
Patient Waiting Time

Purpose This study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized. Design/methodology/approach In this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model. Findings In this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment. Originality/value Today, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.

scholarly journals A Transient Queuing Model for Analyzing and Optimizing Gate Congestion of Railway Container Terminals

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ming Zeng ◽  
Wenming Cheng ◽  
Peng Guo
Keyword(s):  
Container Terminal ◽  
System Optimization ◽  
Optimal Operation ◽  
Optimal Number ◽  
Time Interval ◽  
Sensitivity Testing ◽  
Queuing Model ◽  
Gate System ◽  
Gate Congestion ◽  
Service Channels

As the significant connection between the external and internal of the railway container terminal, the operation performance of the gate system plays an important role in the entire system. So the gate congestion will bring many losses to the railway container terminal, even the entire railway container freight system. In this paper, the queue length and the average waiting time of the railway container terminal gate system, as well as the optimal number of service channels during the different time period, are investigated. AnM/Ek/ntransient queuing model is developed based on the distribution of the arrival time interval and the service time; besides the transient solutions are acquired by the equally likely combinations (ELC) heuristic method. Then the model is integrated into an optimization framework to obtain the optimal operation schemes. Finally, some computational experiments are conducted for model validation, sensitivity testing, and system optimization. Experimental results indicate that the model can provide the accurate reflection to the operation situation of the railway container terminal gate system, and the approach can yield the optimal number of service channels within the reasonable computation time.

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